Highly safe lithium-ion batteries (LIBs) are required for large-scale applications such as electrical vehicles and energy storage systems. A highly stable cathode is essential for the development of safe LIBs. LiFePO4 is one of the most stable cathodes because of its stable structure and strong bonding between P and O. However, it has a lower energy density than lithium transition metal oxides. To investigate the high energy density of phosphate materials, vanadium phosphates were investigated. Vanadium enables multiple redox reactions as well as high redox potentials. LiVPO4O has two redox reactions (V5+/V4+/V3+) but low electrochemical activity. In this study, LiVPO4O is doped with fluorine to improve its electrochemical activity and increase its operational redox potential. With increasing fluorine content in LiVPO4O1-xFx, the local vanadium structure changed as the vanadium oxidation state changed. In addition, the operating potential increased with increasing fluorine content. Thus, it was confirmed that fluorine doping leads to a strong inductive effect and high operating voltage, which helps improve the energy density of the cathode materials.

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity , ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.